Pathology Imaging is one of the last fields in medical imaging yet to be digitized. Compared to other well-developed medical imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), digitized pathology images are characterized by super-high image resolution, non-uniform texture patterns, and densely-structured segments. In addition, the diversity of cancer types leads to constantly-changing image patterns, which makes the digitized pathology images become even more challenging for developing fully-automatic image segmentation algorithms.
Digitized pathology images are created from tissue samples stained with different methods for different diagnosing purposes, such as H&E (hematoxylin and eosin) and IHC (immunohistochemical) staining. Both of these staining methods are widely used in pathology, and H&E staining is particularly common for use in biopsy of suspected cancerous tissue.
Conventional pathology image analysis methods utilize human labor to individually examine and label the stained pathology images. This practice requires a great deal of human labor, is time consuming, and is subject to the subjectivity of the pathologist.
The digitalization of pathology image analysis has seen only small amounts of development. In digital pathology image analysis, digital pathology images are partitioned into meaningful segments, such that the pixels belonging to the same segment share the same features. Conventional techniques for segmenting digital pathology images, involve an operator using software to manually define the various image areas. The operator uses a mouse to control a cursor and outlines the various segments of a region of interest. This conventional technique is extraordinarily time consuming, and suffers from the subjectivity of the operator.
Some conventional automated image analysis techniques use bottom-up analysis. Bottom-up image analysis can be fully automatic, and requires no information outside of the image. In bottom-up analysis techniques, an image is analyzed using only the information contained in the pixels themselves, such as hue, saturation, or intensity. Intensity-based bottom-up techniques, when applied to segmentation tasks, are able to capture local edges and therefore provide relatively precise control of region contour information. However, without global cues, these techniques also carry the drawback of having high rates of false positives.
Other conventional image analysis techniques use top-down analysis. Such techniques use additional information, sometimes in the form of training data, to create models for feature extraction. Training data is labeled by a human operator, and machine learning processes are utilized to build models. When applied, these models are able to extract features from a digital pathology image. As applied to image segmentation tasks, top-down analysis has the benefit of relatively better discrimination power, particularly for images that differ more in texture than intensity. However, top-down techniques have the drawback of possible edge-displacement.
It is therefore desirable to provide a fully automatic image segmentation system and method, designed for digital pathology images, that reduces both the false positives of conventional bottom-up analysis and the edge displacement of conventional top-down analysis.